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Understand how to use Natural Language Processing with the Python programming language
Updated on Sep, 2026
Programming, Data Science, Python
Duration - 23.5 hours
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This course is designed to be your complete online resource for learning how to use Natural Language Processing with the Python programming language.
In the course, we will cover everything you need to learn in order to become a world-class practitioner of NLP with Python.
We'll start off with the basics, learning how to open and work with text and\u00a0PDF files with Python, as well as learning how to use regular expressions to search for custom patterns inside text files.
Afterwards, we will begin with the basics of Natural Language Processing, utilizing the Natural Language Toolkit library for Python, as well as the state-of-the-art Spacy library for ultra-fast tokenization, parsing, entity recognition, and lemmatization of text.
We'll understand fundamental NLP concepts such as stemming, lemmatization, stop words, phrase matching, tokenization, and more!
Next, we will cover Part-of-Speech tagging, where your Python scripts will be able to automatically assign words in text to their appropriate part of speech, such as nouns, verbs, and adjectives, an essential part of building intelligent language systems.
We'll also learn about named entity recognition, allowing your code to automatically understand concepts like money, time, companies, products, and more simply by supplying text information.
Through state-of-the-art visualization libraries, we will be able to view these relationships in real-time.
Then we will move on to understanding machine learning with Scikit-Learn to conduct text classification, such as automatically building machine learning systems that can determine positive versus negative movie reviews or spam versus legitimate email messages.
We will expand this knowledge to more complex unsupervised learning methods for natural language processing, such as topic modelling, where our machine learning models will detect topics and major concepts from raw text files.
This course even covers advanced topics, such as sentiment analysis of text with the NLTK\u00a0library, and creating semantic word vectors with the Word2Vec algorithm.
Included in this course is an entire section devoted to state-of-the-art advanced topics, such as using deep learning to build out our own chatbots!
Not only do you get fantastic technical content with this course, but you will also get access to both our course-related Question and Answer forums, as well as our live student chat channel, so you can team up with other students for projects, or get help on the course content from myself and the course teaching assistants.
All of this comes with a 30-day money-back guarantee, so you can try the course risk-free.
What are you waiting for? Become an expert in natural language processing today!
Learn to work with text files using Python.
Learn how to work with PDF files in Python.
Utilize Regular Expressions for pattern searching in text.
Use Spacy for ultra-fast tokenization.
Learn about Stemming and Lemmatization.
Understand Vocabulary Matching with Spacy.
Use Part of Speech Tagging to automatically process raw text files.
Understand Named Entity Recognition.
Visualize POS and NER with Spacy.
Use SciKit-Learn for Text Classification.
Use Latent Dirichlet Allocation for Topic Modeling.
Learn about Non-negative Matrix Factorization.
Understand general Python.
Check out the detailed breakdown of what’s inside the course
Welcome to the epicenter of innovation, where a collective of visionaries, PhDs, and leading practitioners in Artificial Intelligence, Computer Science, Machine Learning, and Statistics unite. Our team hails from the tech titans - Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.
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